Your Reliability Engineering Professional Development Site
Kirk and Fred and a long-time listener, Nik Sharpe, discuss the long history of doing this podcast from the first Speaking of Reliability podcast SOR #1, “Can you pass HALT?” recorded June 20th, 2015, to this 1000th episode.
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A definition: Maintenance describes the management, control, execution and quality of those activities which will reasonably ensure that design levels of availability and performance of assets are achieved in order to meet business objectives. [Read more…]
Reliability activities serve one purpose: to support better decision making.
That is all it does. Reliability work may reveal design weaknesses, which we can decide to address. Reliability work may estimate the longevity of a device, allowing decisions when compared to objectives for reliability.
Creating a report that no one reads is not the purpose of reliability. Running a test or analysis to simply ‘do reliability’ is not helpful to anyone. Anything with MTBF involved … well, you know how I feel about that. [Read more…]
A simple way of looking at our brain is by dividing it into the conscious, subconscious and unconscious minds. The conscious mind is all about what we are actively thinking about in the here and now. We might be navigating as we drive through the countryside. We might decide to take an exit from the main road because our conscious mind has worked out that the map we are looking at is showing us that’s what we need to do to get to where we want to go.
A PF curve is a graphical tool used in the field of maintenance and reliability. It essentially illustrates a component’s health degradation over its lifetime. As well as a visual guide on when to conduct appropriate action to minimizes operational risks related to unplanned failures. It is essentially a planning tool.
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The following note and question appear in my email the other day. I had given the definition of reliability quite a bit of thought, yet have not really thought too much about a definition of ‘product life time’.
So after answering Najib’s question I thought it may make a good conversation starter here. Give it a quite read, and add how you would answer the questions Najib poses. [Read more…]
Here’s a common problem: You have been tasked with peering into the future to predict when the next failure will occur.
Predictions are tough.
One way to approach this problem is to analyze the history of failures of the most typical system. The issue looms larger when you have only two observed failures from the population of systems in question.
While you can fit a straight line to two failures and account for all the systems that operated without failure, it is not very satisfactory. It is at best a crude estimate.
Let’s not consider calculating MTBF. That would not provide useful information as regular readers already know. So what can you do given just two failures to create a meaningful estimate of future failures? Let’s explore a couple of options. [Read more…]
All models are wrong, some are useful. ~ George E. P. Box
If you know me, you know I do not like MTBF. Trying to predict MTBF, which I consider a worthless metric, is folly.
So, why the article on predicting MTBF?
Predicting MTBF or creating an estimate is often requested by your customer or organization. You are being specifically asked for MTBF for a new product.
You have to come up with something.
Failure Modes and Effect Analysis (FMEA) is an excellent foundation for Reliability Programs. It is also a stepping stone to build Reliability, Availability, and Maintainability (RAM) models. Here lies the “beauty” of doing Reliability and Maintenance analysis. Whereby information from one analysis can help enhance another analysis.
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Recently received a request for my opinion concerning the calculation of system availability using the classic formula
$$ \displaystyle \large A=\frac{MTBF}{MTBF+MTTR}$$
The work is to create a set of goals for various suppliers and contractors to achieve. The calculation values derive from vendor data sheets and available information concerning MTBF and MTTR. The project is in the design phase; thus, they do not have working systems available to measure actual availability.
How would you go about improving on this approach? [Read more…]
Chris and Fred discuss reliability prediction and how it can relate to the ‘design’ phase when there is no data. How do you ‘predict reliability?’
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Chris and Fred discuss how short, 1-minute explainer videos could help reliability engineers … especially new ones!
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The Linkedin NoMTBF group is growing and while not very active does have an occasional interesting discussion. Join the discussion and maybe relate how you have raised awareness around the proper use of MTBF.
https://www.linkedin.com/groups/1857182/
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The following is a recent discussion on the sister Linkedin NoMTBF Group. It was and may continue to be a great discussion. Please take a look and comment on where you stand. Do you have some form of the Arrhenius reaction rate equation in your reliability engineering work? Join the discussion here with a comment or on the Linkedin group conversion.
Fred
[Read more…]As in the non-repairable case, we can fit parametric models to the time between failure data. Two categories of parametric models are presented: the Homogeneous Poisson Process (HPP) and the Non-Homogeneous Poisson Process (NHPP). The HPP implies that the failure rate (ROCOF) is constant over time. The usefulness of the Mean Time Between Failure statistic (MTBF) depends on the type of parametric model used. An HPP model fit is demonstrated using Minitab software since Reliasoft only uses a more general approach. The Power Law (or Power ROCOF) model is introduced, and this model handles non-constant failure rates. An example of a Power Law model is shown using Reliasoft.
Reliability Reference Textbook – Section 11, Pages 10-22
